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Record W2076765408 · doi:10.1371/journal.pone.0079899

European Union’s Public Fishing Access Agreements in Developing Countries

2013· article· en· W2076765408 on OpenAlexafffund
Frédéric Le Manach, Christian Chaboud, Duncan Copeland, Philippe Cury, Didier Gascuel, Kristin M. Kleisner, André Standing, U. Rashid Sumaila, Dirk Zeller, Daniel Pauly

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaMAVA FoundationPew Charitable Trusts
KeywordsEuropean unionFishingBusinessSubsidyFish stockEquity (law)FisheryInternational tradeInternational economicsEconomicsPolitical scienceMarket economy

Abstract

fetched live from OpenAlex

The imperative to increase seafood supply while dealing with its overfished local stocks has pushed the European Union (EU) and its Member States to fish in the Exclusive Economic Zones of other countries through various types of fishing agreements for decades. Although European public fishing agreements are commented on regularly and considered to be transparent, this is the first global and historical study on the fee regime that governs them. We find that the EU has subsidized these agreements at an average of 75% of their cost (financial contribution agreed upon in the agreements), while private European business interests paid the equivalent of 1.5% of the value of the fish that was eventually landed. This raises questions of fisheries benefit-sharing and resource-use equity that the EU has the potential to address during the nearly completed reform of its Common Fisheries Policy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.257
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2013
Admission routes2
Has abstractyes

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